Bootstrap inference for the finite population total under complex sampling designs
报告人: 王中雷,厦门大学王亚南经济研究院
时间:2019-10-24 2:00 - 3:00 pm
地点:Room 217, Guanghua Building 2
Abstract:
Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results under complex survey sampling. Most studies about bootstrap-based inference are developed under simple random sampling and stratified random sampling. In this paper, we propose a unified bootstrap method applicable to some complex sampling designs, including Poisson sampling and probability-proportional-to-size sampling. Two main features of the proposed bootstrap method are that studentization is used to make inference, and the finite population is bootstrapped based on a multinomial distribution by incorporating the sampling information. We show that the proposed bootstrap method is second-order accurate using the Edgeworth expansion. Two simulation studies are conducted to compare the proposed bootstrap method with the Wald-type method, which is widely used in survey sampling. Results show that the proposed bootstrap method is better in terms of coverage rate especially when sample size is limited.
About the Speaker:
王中雷助理教授毕业于爱荷华州立大学统计系,现就职于厦门大学王亚南经济研究院。其研究兴趣包括抽样调查以及重抽样方法。
http://wise.xmu.edu.cn/people/faculty/329d06db-4b6f-4e54-bdd2-4e22479bcee4.html